class_docstr: Registry responsible for built-in keras layers.
docstr: Returns whether the registry supports this layer type.
  
  Args:
    layer: The layer to check for support.
  
  Returns:
    True/False whether the layer type is supported.
docstr_tok: ["Returns","whether","the","registry","supports","this","layer","type",".","Args",":","layer",":","The","layer","to","check","for","support",".","Returns",":","True\/False","whether","the","layer","type","is","supported","."]
code: def supports(cls, layer):
      """Returns whether the registry supports this layer type.
  
      Args:
        layer: The layer to check for support.
  
      Returns:
        True/False whether the layer type is supported.
  
      """
      if layer.__class__ in cls._LAYERS_WEIGHTS_MAP:
          return True
  
      if layer.__class__ in cls._RNN_LAYERS:
          for cell in cls._get_rnn_cells(layer):
              if (
                  cell.__class__ not in cls._RNN_CELLS_WEIGHTS_MAP
                  and not isinstance(cell, prunable_layer.PrunableLayer)
              ):
                  return False
          return True
  
      return False
  
file_path: model-optimization/tensorflow_model_optimization/python/core/sparsity/keras/prune_registry.py
imports: import tensorflow
  from tensorflow_model_optimization.python.core.sparsity.keras import prunable_layer
label_desc: Use image node_id 1 for calling the PruneRegistry obj's underlying member method code with example usage: obj.supports(cls, layer) and returns: False, True, True, False